Abstract

   The incidence of endometrial cancer is increasing each year, and
   treatment effects are poor for patients with advanced and specific
   subtypes. Exploring immune infiltration-related factors in endometrial
   cancer can aid in the prognosis of patients and provide new
   immunotherapy targets. We downloaded immune metagene and functional
   data of patients with different subtypes of endometrial cancer from The
   Cancer Genome Atlas database and selected the lymphocyte-specific
   kinase (LCK) metagene as a representative genetic marker of the immune
   microenvironment in endometrial cancer. The results showed that LCK
   metagene expression is related to the prognosis of patients with
   endometrioid endometrial adenocarcinoma subtypes and highly correlated
   with the PTEN and PIK3CA mutational status. A search for LCK-related
   modules returned seven independent genetic predictors of survival in
   patients with endometrial cancer. The TIMER algorithm showed that the
   expression of these seven genes was positively correlated with the
   infiltration levels of six types of immune cells. The diagnostic value
   of these markers was validated using real-time quantitative PCR and
   immunohistochemical methods. Our results identified CD74, HLA-DRB5,
   CD52, HLA-DPB1 and HLA-DRB1 as possible valuable genetic markers for
   the diagnosis and prognosis of endometrial cancer and provided a
   theoretical basis for immunotherapy targets for its clinical treatment.

   Keywords: endometrial cancer, immune microenvironment, prognosis, CD74,
   CD52

INTRODUCTION

   Endometrial cancer (EC) is an epithelial malignant tumor that occurs in
   the endometrium. In 2019, an estimated 720,000 women living in the
   United States have been diagnosed with EC, and 54,000 cases bas be
   newly diagnosed [[30]1]. According to recent statistics from the
   National Cancer Center of China, new cases of EC in 2015 ranked among
   the top 10 malignant tumors, accounting for 3.88% of all malignant
   tumors in women in China, increased from 3.79% in 2014 [[31]2]. In the
   past ten years, because of the irregular use of hormones and changes in
   people’s living environment and lifestyle, the prevalence and mortality
   of EC have increased [[32]3]. The treatment options for EC include
   surgery, radiotherapy and chemotherapy, hormone therapy, and targeted
   therapy [[33]4]. For patients with advanced metastatic or recurrent EC,
   the rate of treatment failure remains high because of the lost
   opportunity for surgery [[34]5]. Moreover, for specific EC subtypes,
   such as relapsed and endometrial serous carcinoma, the prognosis is
   especially poor [[35]6]. Paclitaxel combined with carboplatin is the
   first-line treatment for advanced recurrent and metastatic EC. In
   addition, platinum drugs and megestrol acetate have been approved for
   the palliative treatment of advanced EC, but the therapeutic effect is
   very limited [[36]7]. Studies have shown that 50% of Caucasian, 21.9%
   of Asian, and 12.5% of Pacific island populations show loss of
   expression of one or more mismatch repair genes [[37]8]. Genetic
   polymorphisms in TGFB1, TGFBR1, SNAI1 and TWIST1 are associated with EC
   susceptibility in Chinese Han women [[38]9].

   From a pathological perspective, EC is a heterogeneous disease with
   widely variable clinical outcomes, both in terms of prognosis and
   treatment response. With the advent of the genetic era, EC has been
   divided into four molecular categories, namely POLE ultra-mutated,
   microsatellite instable (MSI), copy-number low/microsatellite stable
   (MSS), and copy number high/serous-like [[39]10]. POLE-mutated and MSI
   EC have high mutation rates and stronger associations with immunogenic
   tumors. As such, immune checkpoint inhibitors such as PD1/PD-L1
   antibody treatment can be used. In contrast, the copy-number low and
   copy number high types have lower mutation rates, are related to
   non-immunogenic tumors, and, in such cases, combined immunotherapy can
   be used to turn cold tumors into hot tumors [[40]11–[41]12]. Therefore,
   immunotherapy is a potentially useful treatment strategy for patients
   with advanced EC. Although some patients have achieved encouraging
   results with this intervention, some patients do not respond to
   immunotherapy [[42]13]. PD-L1 antibody is widely approved for the
   treatment of MSI type EC, but the incidence of EC MSI is only
   approximately 20% and most patients have the MSS type. MSS EC is
   treated with PD-L1 antibody with a very low effective rate. These
   patients who have progressed after first-line treatment have very
   limited treatment options [[43]14].

   The tumor immune microenvironment is complex and diverse and may affect
   the growth of pre-cancerous cells, directly contrasting the
   immunotherapy of malignant tumors [[44]15]. The immune microenvironment
   is an integral part of the tumor microenvironment (TME). It is mainly
   composed of tumor-infiltrating lymphocytes (TILs) and other immune
   cells that penetrate the tumor tissue. TILs mainly include T cells,
   macrophages, natural killer cells, and dendritic cells. As part of the
   cell-mediated immune response, TILs can lead to the clearing of tumor
   cells [[45]16]. Stimulating the immune system and enhancing the
   anti-tumor function of the TME may be a novel approach for killing
   tumor cells and, to this end, researchers are investigating the
   combined use of various immunological checkpoint-based treatment
   strategies with targeted drugs, local area therapy, and other forms of
   immunotherapy [[46]17]. EC cells can escape attack by the host immune
   system in various manners, such as self-modification and changes in the
   cell surface co-stimulation of molecular expression [[47]18–[48]19],
   which leads to changes in the composition and function of the immune
   microenvironment [[49]20], ultimately leading to tumor immune escape.
   Reversing the immune escape of the tumor is an effective approach for
   inhibiting the progression of EC [[50]21]. The immune escape mechanism
   in the TME of advanced EC is highly heterogeneous. Studies have shown
   that many immune cells often accumulate in and around EC tissues
   [[51]22]. Furthermore, the presence of a large number of CD8^+ T
   lymphocytes and CD45RO^+ T lymphocytes is associated with an increase
   in the overall survival (OS) of patients with EC [[52]23]. Therefore,
   exploring the factors associated with immune infiltration in EC may
   help evaluate the prognosis of these patients and provide new targets
   for immunotherapy.

   In this study, we used a series of bioinformatics tools to determine
   the appropriate immune scoring method for different clinical subtypes
   of EC in The Cancer Genome Atlas (TCGA) database. We identified
   possible correlations between gene expression in the immune
   microenvironment of EC and prognosis. We verified this expression in EC
   and normal tissues and analyzed the relationship between expression and
   the disease-free survival rate. Finally, we identified several genes as
   possible immune microenvironment indicators of prognosis in EC, as well
   as possible targets for immunotherapy.

RESULTS

Selection of the lymphocyte-specific kinase (LCK) metagene as a
representative genetic marker in the immune microenvironment of EC

   Stromal cells, immune cells, and ESTIMATE scores were predicted by
   expression profile data using the ESTIMATE R package. Gene expression
   data were obtained from patients with different EC subtypes in TCGA
   database, and the correlation (cor) between the scores in patients and
   different immunoglobulin genes was calculated using the Spearman
   correlation coefficient ([53]Figure 1A–[54]1C). Functional annotation
   of the immune-system-related metagene clusters is presented in
   [55]Supplementary Table 1. The endometrioid cohort in TCGA database is
   divided into three subtypes: endometrioid endometrial adenocarcinoma,
   serous endometrial adenocarcinoma, and mixed serous and endometrioid.
   In the three EC subtypes, except for the neoantigen score, the LCK
   metagene score showed a significant positive correlation with other
   types of immune-related scores: endometrioid endometrial adenocarcinoma
   (cor = 0.84), serous endometrial adenocarcinoma (cor = 0.83), and mixed
   serous and endometrioid (cor = 0.85). Next, we analyzed the
   distribution of the LCK metagene levels in three EC subtypes at
   different clinical stages of EC. The results revealed no significant
   differences in LCK metagene expression at different clinical stages
   ([56]Figure 1D). Patients with each EC subtype were divided into two
   groups of high expression and low expression of LCK to analyze the
   prognosis of each group ([57]Figure 2A–[58]2C). We observed no
   significant differences in LCK metagene expression between the three
   subtypes in ([59]Figure 2D). Furthermore, the prognostic analysis
   results showed that in the endometrioid endometrial adenocarcinoma
   subtype group, the survival rate of patients with high LCK metagene
   expression was markedly higher than that of the low expression group.

Figure 1.

   [60]Figure 1
   [61]Open in a new tab

   Correlations between different immune scores in patients with different
   endometrial cancer subtypes. (A) Positive correlation between LCK
   metagene score and other types of immune-related scores in endometrioid
   endometrial adenocarcinoma (cor = 0.84). (B) Serous endometrial
   adenocarcinoma (cor = 0.83). (C) Mixed serous and endometrioid (cor =
   0.85). Spearman correlation coefficients are color-coded to indicate
   positive (blue) or negative (red) associations. (D) LCK metagene gene
   expression scores in patients with endometrial cancer at different
   clinical stages. Data are presented as the mean ± SEM. *P < 0.05, ** P
   < 0.01, *** P < 0.001.

Figure 2.

   [62]Figure 2
   [63]Open in a new tab

   Relationship between LCK metagene gene score and prognosis and gene
   mutation in endometrial cancer. (A) Survival curves for endometrioid
   endometrial adenocarcinoma indicated that high expression of LCK
   metagene correlates with better clinical outcomes. (B) Survival curves
   for serous endometrial adenocarcinoma. (C) Survival curves for mixed
   serous and endometrioid. Data were analyzed in KM plotter. (D) LCK
   metagene scores of patients with different subtypes of endometrial
   cancer. (E) Somatic mutation data of PTEN, PIK3CA, TP53, and KRAS. Mut:
   mutant; WT: wild-type. Data are presented as the mean ± SEM. *P < 0.05,
   ** P < 0.01, *** P < 0.001.

   Next, we downloaded the somatic mutation data for PTEN, PIK3CA, TP53,
   and KRAS, which are commonly mutated genes in EC, and divided the
   patients into mutant and wild-type groups. The expression of LCK in the
   PTEN, PIK3CA, TP53, and KRAS groups and difference between the mutant
   and wild-type groups were assessed. The results showed that LCK
   metagene expression was higher in the PTEN and PIK3CA mutant groups
   than in the wild-type group, with no significant difference in LCK
   metagene expression between the TP53 and KRAS mutant and wild-type
   groups ([64]Figure 2E).

   In summary, the LCK metagene is a representative genetic marker in the
   immune microenvironment of EC subtypes and can be used for prognostic
   evaluation of EC.

Screening of representative genes in LCK metagene-related gene modules and
identification of differentially expressed genes (DEGs) in high and low LCK
metagene expression groups

   We next performed hierarchical clustering analysis ([65]Supplementary
   Figure 1A), filtered out samples with distances of >120 as outliers,
   and obtained 546 samples. Weighted gene co-expression network analysis
   (WGCNA) was performed to construct a weighted co-expression network,
   and a β value of 6 was used to ensure a scale-free network
   ([66]Supplementary Figure 1B, [67]1C). A total of 5000 genes were
   assigned to 19 co-expression modules ([68]Supplementary Figure 1D). The
   number of genes corresponding to each module is shown in
   [69]Supplementary Table 2. Two gene sets that could not be aggregated
   into other modules were excluded. We calculated the correlation between
   the feature vectors of the 17 modules and LCK metagene score
   ([70]Figure 3A). The LCK metagene gene score was highly correlated with
   the pink module (R = 0.69). Next, we chose the pink (R = 0.69) module
   for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. This
   module was enriched in 20 pathways related to various aspects of
   immunity, such as antigen processing and presentation, Th1 and Th2 cell
   differentiation, and cell adhesion molecules ([71]Figure 3B). The
   limma-voom method was used to analyze the genetic differences between
   the high and low LCK expression groups, and 2,524 DEGs were obtained
   ([72]Figure 3C). In the LCK high expression group, there were
   significantly more up-regulated genes than down-regulated genes.

Figure 3.

   [73]Figure 3
   [74]Open in a new tab

   Screening of representative genes in LCK metagene-related gene. (A)
   Correlation between eigenvectors of 17 gene modules and LCK metagenes.
   (B) KEGG pathway enrichment analysis in pink module. (C) Volcano maps
   of DEGs. Red represents genes upregulated in patients with high LCK
   metagene scores, while blue represents genes downregulated in patients
   with low LCK metagene scores.

Exploration of prognostic markers related to the immune microenvironment of
EC

   We then integrated the 141 genes from the pink modules of the LCK
   metagene and 2,524 DEGs between the high and low LCK metagene
   expression groups. Integration of the 70 selected genes
   ([75]Supplementary Table 3), excluding 12 known immune-related
   metagenes, resulted in 58 genes ([76]Figure 4A) ([77]Supplementary
   Table 4). The R package clusterProfiler was used for KEGG enrichment
   analysis of these genes using a false discovery rate of <0.05 as the
   threshold ([78]Figure 4B). Fifty-eight genes were enriched in 20
   pathways, most of which were immune disease-related. The R package
   STRINGdb was used to analyze the protein network interaction of these
   58 genes. After mapping these genes to the STRING database, a
   relationship network with 134 edges and 34 nodes was obtained
   ([79]Figure 4C). Analysis of the distribution of nodes in the network
   ([80]Figure 4D) showed that the connection degree of each node was very
   high (4.17, on average), indicating that these genes are closely
   related.

Figure 4.

   [81]Figure 4
   [82]Open in a new tab

   Screening of prognostic markers related to the immune microenvironment
   of endometrial cancer. (A) Veen diagram analysis showed co-expressed
   genes significantly associated with LCK metagene. (B) Gene KEGG pathway
   enrichment analysis showed 58 genes enriched in 20 pathways. The false
   discovery rate <0.05 as the threshold. (C) Protein interaction networks
   of these 58 genes. (D) The degree distribution of nodes in the network.

Prognostic markers related to the immune microenvironment of EC

   Next, we performed univariate cox survival analysis of the EC TCGA
   database to analyze the relationship between the expression of these 58
   genes and patient prognosis. We subsequently included the clinical
   stage as a covariate in the analysis, with p-values < 0.05 as the
   threshold, to exclude its impact. A total of 11 genes met these
   conditions: CD74, HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1, TNFRSF1B, IGHA1,
   ODF3B, ACP5, LAPTM5, and IGLC2. High expression of these genes was
   strongly correlated with prognosis, and we finally obtained 11
   independent prognostic factors, as shown in [83]Supplementary Table 5.
   The g: profiler was used to analyze the GO terms of these 11 genes.
   Four of these genes (IGHA1, LAPTM5, ODF3B, and IGLC2) were not enriched
   for any GO term and were eliminated. Finally, seven genes, CD74,
   HLA-DRB5, CD52, HLA-DPB1, HLA -DRB1, TNFRSF1B, and ACP5, were selected.
   The results showed 156 enriched GO terms associated with these 58
   genes, most of which were related to immunity ([84]Supplementary Table
   6), including antigen processing, peptide or polysaccharide antigen
   presentation via MHC class II, regulation of T cell proliferation, and
   regulation of immune response-related cytokine production.

   Samples from patients with EC in TCGA were divided into two groups
   according to the median expression levels of the seven
   prognosis-related genes of the EC immune microenvironment. Prognostic
   differences between the high and low expression groups of these seven
   genes were analyzed. The results showed that patients with high
   expression of these seven genes had higher survival rates ([85]Figure
   5A).

Figure 5.

   [86]Figure 5
   [87]Open in a new tab

   Correlation of microenvironment related prognostic genes’ expression
   with immune infiltration level. (A) Kaplan-Meier survival curve of 7
   microenvironment related prognostic signature. (B) Immune cell
   infiltration analysis. A correlation coefficient of <0.3 indicates no
   correlation and a value of >0.3 indicates a positive correlation. (C)
   UALCAN website analysis CD74, HLA-DRB5, HLA-DRB1 and ACP5 protein
   expression. *P < 0.05, ** P < 0.01, *** P < 0.001.

   Next, we used the tumor immune estimation resource (TIMER) algorithm to
   analyze six infiltrating-immune cells (CD4^+ T cells, CD8^+ T cells, B
   cells, neutrophils, macrophages, and dendritic cells) in the uterus and
   the correlation between the expression of the seven selected genes and
   level of immune infiltration. The results showed that the expression of
   CD74, HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1, TNFRSF1B, and ACP5 was
   significantly positively correlated with the level of immune
   infiltration ([88]Figure 5B). We analyzed the protein expression of
   these seven genes in EC tissues using the online tool UALCAN
   ([89]http://ualcan.path.uab.edu/index.html). Available data on the
   UALCAN platform showed revealed the CD74, HLA-DRB5, HLA-DRB1, and ACP5
   protein levels in EC tissues and normal endometrial tissues ([90]Figure
   5C). CD52 and TNFRSF1B protein expression was not predicted.

Specimen verification

   Next, we detected the mRNA expression of CD74, HLA-DRB5, CD52,
   HLA-DPB1, HLA-DRB1, TNFRSF1B, and ACP5 in 41 EC tissues and 20 normal
   endometrial tissues by real-time PCR. The results showed that the
   expression levels of CD74, HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1 were
   higher in EC tissues than in normal endometrial tissues ([91]Figure
   6A). Receiver operating characteristic (ROC) curve analysis was
   performed, and the correlation area under the curve was used to confirm
   the diagnostic efficacy of the gene expression levels ([92]Figure 6B).
   The results suggest that CD74, HLA-DRB5, CD52, HLA-DPB1 and HLA-DRB1
   expression levels can discriminate between EC and normal endometrial
   tissue. Furthermore, CD74, HLA-DRB5, CD52, HLA-DPB1 and HLA-DRB1
   protein expression levels were detected by immunohistochemistry in 42
   EC tissues and 20 normal endometrial tissues. The results showed that
   the high expression rate of CD74 protein expression in early-stage EC
   was 54.5%, which was higher than that of in normal endometrial tissue
   (20%, P = 0.0289). The high expression rate of HLA-DRB5 protein
   expression in early-stage EC was 59.1%, which was higher than that of
   in normal endometrial tissue (25%, P = 0.0334). The high expression
   rate of CD52 protein expression in early-stage EC was 63.6%, which was
   higher than that of in normal endometrial tissue (25%, P = 0.0157). The
   high expression rate of HLA-DPB1 protein expression in early-stage EC
   was 54.5%, which was higher than that of in normal endometrial tissue
   (20%, P = 0.0068). The high expression rate of HLA-DRB1 protein
   expression in early-stage EC was 72.2%, which was higher than that of
   in normal endometrial tissue (40%, P = 0.0124). These five proteins
   were highly expressed in early-stage EC tissues compared to in normal
   endometrial tissue. In advanced EC tissues, there was not significant
   difference in the high expression rate of these five proteins compared
   to in normal tissues. The disease-free survival curves indicated that
   high expression of CD52 and HLA-DPB1 is correlated with high survival
   rates in EC ([93]Figure 6C).

Figure 6.

   [94]Figure 6
   [95]Open in a new tab

   CD74, HLA-DRB5, CD52, HLA-DPB1, and HLA-DRB1 in the microenvironment of
   related prognostic markers in endometrial cancer. (A) Expression of
   CD74, HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1, TNFRSF1B, and ACP5 in 41
   endometrial cancer tissues and 20 normal tissues was determined by
   qRT-PCR. (B) ROC curve of 5 msicroenvironment-related prognostic
   signature. (C) Expression of CD74, HLA-DRB5, CD52, HLA-DPB1 and
   HLA-DRB1 was detected by immunohistochemistry in endometrial cancer (n
   = 42) and normal endometrial tissue (n = 20). Disease-free survival
   curves for CD74, HLA-DRB5, CD52, HLA-DPB1 and HLA-DRB1 in 42
   endometrial carcinoma cases. *P < 0.05, **P < 0.01, ***P < 0.001.

DISCUSSION

   Abundant infiltrating-immune cells and cytokines are typically observed
   in EC tissues, which can stimulate the endogenous anti-tumor immune
   response [[96]24], indicating that patients with EC may benefit from
   immunotherapy. Exploring genes related to the EC immune environment
   that can predict prognosis is a pivotal step for finding treatment
   targets for immunotherapy.

   In this study, we first assessed the correlation between different
   types of EC and different immune-related scores by analyzing different
   EC subtypes in TCGA database: endometrioid endometrial adenocarcinoma,
   serous endometrial adenocarcinoma, and mixed serous and endometrioid.
   The results showed that the LCK metagene score was the highest relative
   to other types of immune-related scores. Studies have shown that in
   breast cancer, the LCK metagene has a high co-expression level with
   immune characteristics and is significantly positively correlated with
   the histological TIL count. This single representative measure of
   immune infiltration is correlated with global genomic metrics. In one
   study, microarray analysis of 1,781 primary breast cancer samples in 12
   data sets was performed to determine the correlation between immune
   system-related metagenes and clinical parameters and survival rates. A
   large cluster of nearly 600 genes with functions in immune cells was
   consistently obtained in all datasets, among them, the LCK metagene
   showed very high immune prognostic value. In ER-negative and HER2
   overexpression ER-positive EC, patients with high expression of LCK had
   a better prognosis [[97]25–[98]26]. In EC, no metagene has been
   reported to be related to the immune microenvironment. Based on the
   prominent role of the LCK metagene in breast cancer, it was used as a
   research target in EC. Our results showed that the LCK metagene is
   strongly correlated with the immune microenvironment of EC. The LCK
   metagene consists of 47 genes (ARHGAP15, ARHGAP25, CCL5, CCR2, CCR7,
   CD2, CD247, CD27, CD3D, CD48, CD53, CORO1A, CSF2RB, EVI2B, FGL2,
   GIMAP4, GIMAP5, GMFG, GZMA, HC1, GZM, IL2RG, IL7R, INPP5D, IRF8, ITK,
   KLRK1, LCK, LCP2, LPXN, LTB, PIK3CD, PLAC8, PRG1, PRKCB1, PTPRC, RAC2,
   SAMSSN1, SCYA5, SELL, SD2D1A, SLA, SLAMF1), which are all directly or
   indirectly involved in T cell-mediated immunity. For example, CCL5 is a
   chemotactic agent for memory T helper cells and eosinophils
   [[99]27–[100]28]. Moreover, the protein encoded by CD27 is a member of
   the TNF receptor superfamily. This receptor is necessary for the
   generation and long-term maintenance of T cell immunity. It binds to
   ligand CD70 and plays a key role in regulating B cell activation and
   immunoglobulin synthesis [[101]29]. LCK is a cytoplasmic tyrosine
   kinase of the Src family expressed in T cells and natural killer cells.
   It is relatively specific in lymphocytes, particularly in mature
   resting T lymphocytes, activating signal transduction in T cells and
   playing an essential regulatory role of differentiation. LCK activation
   is a core step in T cell activation. Before this step, LCK forms a
   non-covalent bond with the CD4 and CD8 complex receptors via cysteine
   at the N-terminus [[102]30]. Therefore, selective inhibition of LCK can
   be used to treat T cell-mediated autoimmune diseases, inflammatory
   diseases, and organ transplant rejection [[103]31–[104]32]. Stimulating
   an abnormal LCK signal to enhance the reset of the PD-1 blockade has
   become a new targeted molecular approach for cancer treatment
   [[105]33].

   TCGA database analysis showed that in the endometrioid endometrial
   adenocarcinoma subtype, the prognosis of patients with high LCK
   expression was significantly better than that of the low expression
   group. These results suggest that the LCK metagene is a prognostic
   marker in EC. The most common mutant genes of EC were PTEN, PIK3CA,
   TP53, and KRAS [[106]34]. We downloaded the somatic mutation data of
   these four genes and divided the patients into mutant and wild-type
   groups. The LCK metagene expression of each group was analyzed. Among
   them, expression of the LCK metagene was significantly increased in the
   PTEN and PIK3CA mutant groups. PTEN mutation or deletion is one of the
   most significant molecular characteristics of EC. The mutation rates in
   low- and high-grade endometrioid carcinomas are 67.0% and 90.0%,
   respectively, and 2.7% in serous carcinomas [[107]35]. The oncogene
   PIK3CA has a mutation rate of 52% in type I EC and 33% in type II EC
   [[108]36–[109]37]. Compared with PTEN mutations that occur in the early
   stages of the lesion, PIK3CA mutations tend to occur in the middle and
   late stages of disease. Furthermore, PIK3R1 mutations destabilize PTEN,
   which is a key event leading to tumor development [[110]38]. Therefore,
   as a representative metagene in the immune microenvironment of EC, the
   LCK metagene is a potential research target.

   Next, we used the LCK metagene members as the core object and performed
   WGCNA to detect representative genes from LCK-related gene modules and
   constructed a weighted co-expression network. We also analyzed the DEGs
   between samples with high and low LCK metagene scores to identify
   co-expressed genes whose mRNA levels were significantly correlated with
   LCK metagenes. By evaluating the overlap between co-expressed genes
   significantly related to LCK and exploring the functions of these genes
   through enrichment analysis, we found multiple enriched immune-related
   GO terms, particularly the T cell receptor signaling pathway and T cell
   activation. Survival analysis and prediction revealed seven potential
   immune-related diagnostic and prognostic markers. These included CD74,
   HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1, TNFRSF1B, and ACP5. We used the
   TIMER algorithm to calculate the correlation between the expression of
   these seven genes and degree of infiltration of CD4^+ T cells, CD8^+ T
   cells, B cells, neutrophils, macrophages, and dendritic cells. All
   genes were significantly positively correlated with cellular
   infiltration.

   HLA is a highly genetically polymorphic group of genes that is the main
   component of specific immune recognition and the immune response in the
   body [[111]39]. HLA complexes are composed of many genes and can be
   approximately divided into three categories: class I and class II
   molecules are the main types involved in antigen presentation and
   related immune responses. HLA-I includes HLA-A, HLA-B, and HLA-C;
   HLA-II includes HLA-DR, HLA-DQ, and HLA-DP. After MHC-I binds to the
   peptide, it is presented on the cell surface for recognition by CD8^+ T
   cells; HLA-II molecules bind to CD4 on CD4^+ T cells and help the T
   cell antigen receptor transmit activation signals to T cells to promote
   their activation. Eradication of tumors by the immune system depends on
   the effective activation of T cell responses [[112]40]. Studies have
   shown that the high expression level of MHC-II molecules in
   hepatocellular carcinoma tissues is an effective prognostic marker of
   prolonged relapse-free survival time in liver cancer [[113]41]. Baccar
   et al. performed HLA-II staining of 80 surgically resected breast
   malignant and non-malignant tissue sections. The results showed that
   CD99 (+) HLA-II (-) was the worst prognostic marker [[114]42]. These
   findings suggest that in EC, HLA-DRB1 and HLA-DRB5 are new markers for
   the prognosis of patients and provide new targets for targeted therapy
   through T cell activation.

   CD74, the constant chain of MHC-II, can assist it in reaching the
   acidic endosome compartment for intracellular antigen processing,
   participate in MHC-II-mediated antigen presentation, and play an
   important role in the occurrence and development of tumors [[115]43].
   For many years, studies of cancer immunotherapy focused on cytotoxic
   CD8 T cells. However, stimulation of CD4 helper T cells is essential
   for promoting and maintaining immune memory. Therefore, a good
   therapeutic target should cause a two-dimensional T cell response. CD74
   is necessary for the MHC class II heterodimer to correctly guide cells
   to load peptides and be expressed on the surface of antigen-presenting
   cells. Mensali et al. showed that CD74-expressing dendritic cells can
   prime CD4 and CD8 T cells from a naïve population [[116]44]. In EC
   tissues, positive CD4 and CD8 are good prognostic markers, and PD-L1
   and CD4^+ helper T cells may be suitable targets for improving the
   survival rate by enhancing chemical sensitivity [[117]45]. In brain
   metastatic tumor cells, the highly expressed CD74 promotes the normal
   processing mechanism of HLA-II and binding of complex HLA peptides,
   which is crucial for improving the prognosis of patients [[118]46].
   These studies provide useful information showing that CD74 can be used
   as a treatment strategy to incorporate immunotherapy into EC. CD52 can
   be expressed on the cell membrane surface of B-lymphocytic tumors, and
   targeted therapy using anti-CD52 monoclonal antibodies has attracted
   increased attention at home and abroad. This single drug or combined
   chemotherapy can benefit some patients with refractory and relapsed CLL
   [[119]47].

   Then, we analyzed the diagnostic efficacy and prognostic value of CD74,
   HLA-DRB5, CD52, HLA-DPB1, HLA-DRB1, TNFRSF1B, and ACP5 in EC. We
   detected the expression of these seven genes in EC and normal
   endometrial tissue by real-time PCR. The results showed that CD74,
   HLA-DRB5, CD52, HLA-DPB1, and HLA-DRB1 were significantly overexpressed
   in EC tissues. In EC, the functions of CD74, HLA-DRB5, CD52, HLA-DPB1,
   and HLA-DRB1 are poorly understood. Moreover, the ROC results showed
   that the expression levels of these genes can distinguish EC from
   normal endometrial tissue. Immunohistochemical analysis revealed that
   CD74, HLA-DRB5, CD52, HLA-DPB1, and HLA-DRB1 were highly expressed in
   early-stage EC tissues compared to in normal endometrial tissue. In
   advanced EC tissues, there was no significant difference in the high
   expression rate of these five proteins compared to in normal tissues.
   Survival analysis showed that patients highly expressing CD52 and
   HLA-DPB1 had longer disease-free survival. The expression of CD74,
   HLA-DRB5, and HLA-DRB1 in the high and low expression groups was not
   significantly related to survival rates. Combined with the survival
   curve of the prognostic signature from TCGA dataset shown in
   [120]Figure 5A and immune cell infiltration analysis shown in
   [121]Figure 5B, in EC, CD74, HLA-DRB5, and HLA-DRB1 are potential
   microenvironment-related prognosis factors. The EC microenvironment is
   very complex, the composition of which and its correlation with EC
   prognosis remain poorly understood compared to other malignancies.
   Thus, studies of larger sample sizes and involving in vitro experiments
   are needed. Our results provide new prognostic assessment and targets
   for drug therapy for patients with EC and can guide individualized
   cancer immunotherapies.

   In this research, using ESTIMATE algorithm-based immune scoring and
   TCGA EC cohort information analysis, immune-related genes of EC were
   screened and prognostic characteristics were established. Notably,
   through specimen verification, we found that the CD74, HLA-DRB5, CD52,
   HLA-DPB1 and HLA-DRB1 proteins are high expressed in early-stage EC
   tissues. Patients with high expression of CD52 and HLA-DPB1 show better
   prognosis. In summary, our research provides targets in the immune
   microenvironment for the molecular therapy of EC.

MATERIALS AND METHODS

Data sources and pre-processing

   TCGA-UCEC standardized FPKM data
   ([122]https://bioinformatics.mdanderson.org/), somatic mutation, and
   clinical information were downloaded from the UCSC Xena official
   website ([123]https://xena.ucsc.edu/). The 13 types of immune metagenes
   and immune function data were downloaded from DOI: 10.1158/0008-5472.
   CAN-16-3478. The scores of 13 types of immune metagenes were obtained
   by calculating the average value of log2 (FPKM + 1). Six types of
   immune cell scores (B_cell, CD4_Tcell, CD8_Tcell, neutrophil,
   macrophage, dendritic) were obtained from mRNA expression data using
   the TIMER package ([124]https://cistrome.shinyapps.io/timer/). The
   StromalScore and ImmuneScore were calculated using the ESTIMATE R
   package
   ([125]https://bioinformatics.mdanderson.org/estimate/index.html).
   ESTIMATE R package uses expression profile data to predict the scores
   of stromal cells and immune cells, and then predicts the content of
   these two cells; ImmuneSore: immune cell score, StromalScore: stromal
   cell score ESTIMATEScore: comprehensive score. The expression of the
   CD74, HLA-DRB5, HLA-DRB1, and ACP5 proteins in EC tissues was analyzed
   using the online tool UALCAN
   ([126]http://ualcan.path.uab.edu/index.html).

Screening representative genes in the EC immune microenvironment

   The Spearman correlation coefficient was used to calculate the
   correlation between different immune-related scores in different EC
   subtypes. The results showed that the LCK metagene score was the
   highest relative to other types of immune-related scores. Among the
   three subtypes (endometrioid endometrial adenocarcinoma: 0.84, serous
   endometrial adenocarcinoma: 0.83, mixed serous and endometrioid: 0.85),
   LCK metagene was selected as representative of the EC immune
   microenvironment gene. The samples were divided into two groups
   according to the median of LCK metagene mRNA expression level, and the
   Kaplan-Meier (KM) survival curves of the two groups were drawn. We also
   analyzed the relationship between the LCK metagene mRNA expression
   level and PTEN, PTK3CA, TP53, and KRAS mutations.

Analysis of LCK metagene-related modules by WGCNA

   Screening for genes with a median absolute deviation of the top 75% and
   genes with a MAD greater than 0.01 was performed using the WGCNA R
   package to construct a gene co-expression network with genetic methods
   used to generate a dynamic shear module and for cluster analysis of the
   module. Genes with similar expression levels were divided into the same
   module; the important parameters were as follows: minModuleSize = 30,
   merge CutHeight = 0.25. The results showed that the 141 genes in the
   pink module were highly correlated with the LCK metagene (cor = 0.69).
   The clusterProfiler R package was used for KEGG analysis of this module
   (FDR < 0.05).

Screening immune microenvironment genes related to prognosis

   According to the LCK metagene score, the samples were divided into two
   groups of LCK high and low, and the differential expression analysis of
   genes was performed using the limma R package (FDR < 0.1, |log[2]
   (fold-change)| > 1), and KEGG analysis was performed on the 58 genes of
   the intersection. In order to identify genes with prognostic value in
   the immune microenvironment, we performed univariate cox survival
   analysis and used the survminer R package to draw KM survival curves.

Patients and samples

   EC and normal endometrial tissues were collected during surgical
   treatment at Shengjing Hospital Affiliated to China Medical University
   from 2011 to 2017. Fresh tissues included 41 EC tissues and 20 normal
   endometrial tissues, which were evaluated by PCR. There were 66
   paraffin-embedded specimens, including 42 EC tissues and 20 normal
   endometrial tissues, with 22 cases of early EC and 20 cases of advanced
   EC. The inclusion criteria were as follows: 1. The patient had never
   been administered radiochemotherapy and other anti-tumor treatment
   before specimen collection. 2. The patient had no history of other
   gynecological malignancies or metabolic and infectious diseases. Under
   sterile conditions, fresh tissue samples were obtained and stored at
   -80°C. All pathological diagnoses were verified by two pathologists.
   The clinical data of all enrolled patients were collected and counted,
   including patient age, pathological type, FIGO stage, degree of
   differentiation, muscular layer infiltration, and lymph node
   metastasis, and informed consent was obtained from all subjects. This
   study was approved by the Ethics and Ethics Committee of Shengjing
   Hospital Affiliated to China Medical University.

RNA extraction and quantitative RT-PCR

   RNA was extracted from tissues and cells using TRIzol (Takara, Shiga,
   Japan), According to the instructions of the PrimeScript™ RT reagent
   Kit with gDNA Eraser (Takara), reverse transcription was performed.
   qRT-PCR was conducted according to the instructions of the SYBR® TB
   Green™ Premix Ex Taq II (Takara). PCR-specific primers were designed by
   Sangon Biotech Co., Ltd. (Shanghai, China). The fold-change in
   expression was calculated using the 2^-ΔΔCt method, with GAPDH used as
   an internal control. The primer sequences are listed in
   [127]Supplementary Table 7.

Immunohistochemistry

   Histopathological specimens were fixed in formalin and embedded in
   paraffin, and 5-μm serial sections were prepared. After antigen repair
   operation, immunohistochemistry analysis was performed using a kit
   (ZSGB-BIO, Beijing, China). After saturating the endogenous peroxidase
   activity with 3% H[2]O[2] and blocking with goat serum, the prepared
   antibody was added dropwise and incubated overnight at 4°C. The primary
   antibodies information are listed in Supplementary Material 8:
   [128]Supplementary Table 8. On the next day, the samples were washed
   with PBS. After horseradish peroxidase-conjugated secondary antibody
   was added dropwise, DAB color development and hematoxylin
   counterstaining were performed and the samples were evaluated by
   microscopy. The scoring method is based on whether the cell cytoplasm
   has a brownish yellow or brown color as a positive result. No staining,
   0; light yellow, 1; yellow, 2; and brown and sepia, 3. According to the
   percentage of positive cells, the mean value after scoring, with
   negative count as 0, the scores were assigned as follows: percentage of
   positive cells less than 10%, 1; ≥10–50%, 2; >50–75%, 3; and ≥75%, 4.
   The product of 2 scores was considered as the total score, and the
   results were interpreted as follows: ≤2, negative; 3–4, weak positive
   (+); 5–8, medium positive (++); and 9–12, strong positive (+++). Low
   expression was indicated by -/+, and high expression was indicated by
   ++/+++, respectively. The results were evaluated by two senior
   pathologists who were blinded to the patients’ data, and each slice was
   independently observed to determine the positive cell count and
   evaluate the background. In cases of disagreement, a third pathologist
   made the judgement.

Statistical analysis

   GraphPad Prism 8 software (GraphPad, Inc., La Jolla, CA, USA) was used
   for statistical analysis of the experimental data. All data are
   expressed as the mean ± SEM. Student’s t-test was used to compare
   differences between the two groups of samples. Survival curves were
   plotted using the results of Kaplan-Meier (KM) analysis, and
   disease-free survival was defined as the time from the date of
   diagnosis to the time of progression/death or last follow-up. P < 0.05
   was defined as statistically significant.

Ethics approval

   The study protocol was reviewed and approved by the Scientific Research
   and New Technology Ethical Committee of the Shengjing Hospital of China
   Medical University. Ethical number: 2018PS251K.

Supplementary Material

   Supplementary Figure 1
   [129]aging-12-104083-s001..pdf^ (567.5KB, pdf)
   Supplementary Tables 1, 2, 3, 4 and 5
   [130]aging-12-104083-s002..pdf^ (444.7KB, pdf)
   Supplementary Table 6
   [131]aging-12-104083-s003..docx^ (24.2KB, docx)
   Supplementary Tables 7 and 8
   [132]aging-12-104083-s004..pdf^ (391.8KB, pdf)

ACKNOWLEDGMENTS